Sea fog detection based on unsupervised domain adaptation

Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other types of clouds, such as textures and colors, it can be achieved by using image processing methods. Currently, most of the available methods are data-driven and relying on manual annotations. However, because few meteorological observations and buoys over the sea can be realized, obtaining visibility information to help the annotations is difficult. Considering the feasibility of obtaining abundant visible information over the land and the similarity between land fog and sea fog, we propose an unsupervised domain adaptation method to bridge the abundant labeled land fog data and the unlabeled sea fog data to realize the sea fog detection. We used a seeded region growing module to obtain pixel-level masks from rough-labels generated by the unsupervised domain adaptation model. Experimental results demonstrate that our proposed method achieves an accuracy of sea fog recognition up to 99.17%, which is nearly 3% higher than those vanilla methods.